Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domains

TL;DR

This study evaluates large language models' ability to follow concept annotation guidelines in sentence labeling tasks across scientific and financial domains, revealing scale and proprietary model advantages and highlighting gaps in open-source models.

Contribution

The paper introduces a systematic evaluation of LLMs' capacity to follow in-context concept guidelines, comparing open-source and proprietary models across different contexts.

Findings

Larger models improve task performance with concept definitions.

Proprietary models recognize nonsensical guidelines better.

Fine-tuning outperforms scale increases in model effectiveness.

Abstract

Although large language models (LLMs) exhibit remarkable capacity to leverage in-context demonstrations, it is still unclear to what extent they can learn new concepts or facts from ground-truth labels. To address this question, we examine the capacity of instruction-tuned LLMs to follow in-context concept guidelines for sentence labeling tasks. We design guidelines that present different types of factual and counterfactual concept definitions, which are used as prompts for zero-shot sentence classification tasks. Our results show that although concept definitions consistently help in task performance, only the larger models (with 70B parameters or more) have limited ability to work under counterfactual contexts. Importantly, only proprietary models such as GPT-3.5 and GPT-4 can recognize nonsensical guidelines, which we hypothesize is due to more sophisticated alignment methods. Finally, we find that Falcon-180B-chat is outperformed by Llama-2-70B-chat is most cases, which indicates that careful fine-tuning is more effective than increasing model scale. Altogether, our simple evaluation method reveals significant gaps in concept understanding between the most capable open-source language models and the leading proprietary APIs.